polars.from_numpy#

polars.from_numpy(data: np.ndarray[Any, Any], columns: Sequence[str] | None = None, orient: Orientation | None = None) DataFrame[source]#

Construct a DataFrame from a numpy ndarray. This operation clones data.

Note that this is slower than creating from columnar memory.

Parameters:
datanumpy.ndarray

Two-dimensional data represented as a numpy ndarray.

columnsSequence of str, default None

Column labels to use for resulting DataFrame. Must match data dimensions. If not specified, columns will be named column_0, column_1, etc.

orient{None, ‘col’, ‘row’}

Whether to interpret two-dimensional data as columns or as rows. If None, the orientation is inferred by matching the columns and data dimensions. If this does not yield conclusive results, column orientation is used.

Returns:
DataFrame

Examples

>>> import numpy as np
>>> data = np.array([[1, 2, 3], [4, 5, 6]])
>>> df = pl.from_numpy(data, columns=["a", "b"], orient="col")
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 4   │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2   ┆ 5   │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3   ┆ 6   │
└─────┴─────┘